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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ======================================
"""Library of TPU helper functions."""
import collections
import enum
from typing import Any, Callable, Iterable, List, Optional, Text, Tuple, Union
from absl import logging
import numpy as np
from tensorflow.compiler.tf2xla.python import xla as tf2xla
from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.protobuf.tpu import dynamic_padding_pb2 as dynamic_padding
from tensorflow.core.protobuf.tpu import tpu_embedding_configuration_pb2 as embedding_pb2
from tensorflow.python import tf2
from tensorflow.python.compiler.xla import xla
from tensorflow.python.framework import auto_control_deps
from tensorflow.python.framework import composite_tensor
from tensorflow.python.framework import config
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import func_graph
from tensorflow.python.framework import function
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import array_ops_stack
from tensorflow.python.ops import cond
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.tpu import device_assignment as device_assignment_lib
from tensorflow.python.tpu import tensor_tracer
from tensorflow.python.tpu import tpu_feed
from tensorflow.python.tpu import tpu_function
from tensorflow.python.tpu import tpu_name_util
from tensorflow.python.tpu import tpu_replication
from tensorflow.python.tpu.ops import tpu_ops
from tensorflow.python.types import core as core_types
from tensorflow.python.util import compat
from tensorflow.python.util import nest
from tensorflow.python.util import object_identity
from tensorflow.python.util import traceback_utils
from tensorflow.python.util import variable_utils
from tensorflow.python.util.tf_export import tf_export
ops.NotDifferentiable("TPUReplicatedInput")
# Ops which can be safely pruned from XLA compile if they have no consumers.
# These ops should also have no inputs.
_UNCONNECTED_OPS_TO_PRUNE = set(["Placeholder", "VarHandleOp"])
_POST_DEVICE_REWRITE_ATTR = "_post_device_rewrite"
_TPU_COMPILATION_STATUS_ATTR = "_tpu_compilation_status"
_PIVOT_FOR_CLUSTER = "_pivot_for_cluster"
core = tpu_name_util.core
def _tpu_system_device_name(job: Optional[Text]) -> Text:
"""Returns the device name for the TPU_SYSTEM device of `job`."""
if job is None:
return "/device:TPU_SYSTEM:0"
else:
return "/job:%s/device:TPU_SYSTEM:0" % job
@tf_export(v1=["tpu.initialize_system"])
def initialize_system(
embedding_config: Optional[embedding_pb2.TPUEmbeddingConfiguration] = None,
job: Optional[Text] = None,
compilation_failure_closes_chips: bool = True,
tpu_cancellation_closes_chips: Optional[bool] = None,
) -> core_types.Tensor:
"""Initializes a distributed TPU system for use with TensorFlow.
Args:
embedding_config: If not None, a `TPUEmbeddingConfiguration` proto
describing the desired configuration of the hardware embedding lookup
tables. If embedding_config is None, no hardware embeddings can be used.
job: The job (the XXX in TensorFlow device specification /job:XXX) that
contains the TPU devices that will be initialized. If job=None it is
assumed there is only one job in the TensorFlow flock, and an error will
be returned if this assumption does not hold.
compilation_failure_closes_chips: Set the configuration whether
we want to close TPU chips when there is a compilation failure.
tpu_cancellation_closes_chips: Set the configuration whether
we want to close TPU chips when a TPU execution is cancelled. If the value
is None, the behavior will be determined by the command line flag
`tpu_cancellation_closes_chips` for the TPU worker. WARNING: this argument
only applies to TFRT TPU runtime.
Returns:
A serialized `TopologyProto` that describes the TPU system. Note:
the topology must be evaluated using `Session.run` before it can be used.
"""
config_string = ("" if embedding_config is None else
embedding_config.SerializeToString())
# The enum is defined in core/tpu/kernels/tpu_execute_op_options.h.
tpu_cancellation_closes_chips_enum = 0
if tpu_cancellation_closes_chips is not None:
if tpu_cancellation_closes_chips:
tpu_cancellation_closes_chips_enum = 1
else:
tpu_cancellation_closes_chips_enum = 2
with ops.device(_tpu_system_device_name(job)):
topology = tpu_ops.configure_distributed_tpu(
compilation_failure_closes_chips=compilation_failure_closes_chips,
tpu_cancellation_closes_chips=tpu_cancellation_closes_chips_enum,
)
if embedding_config is None:
return topology
# This set of control dependencies is needed as this function is expected to
# return an op which will return the topology when executed, but we need to
# call the embedding initialization op between initializing the TPU and
# returning the topology.
with ops.control_dependencies([topology]):
embedding_init = tpu_ops.configure_tpu_embedding(config=config_string)
with ops.control_dependencies([embedding_init]):
return array_ops.identity(topology, name="tpu_init_identity")
def initialize_system_for_tpu_embedding(
embedding_config: embedding_pb2.TPUEmbeddingConfiguration,
job: Optional[Text] = None,
) -> ops.Operation:
"""Initializes a distributed TPU Embedding system for use with TensorFlow.
The following two are equivalent:
1. initialize_system() with embedding_config.
2. initialize_system() without embedding_config, then
initialize_system_for_tpu_embedding().
initialize_system() should not be called with embedding_config if
initialize_system_for_tpu_embedding() is meant to be called later.
Args:
embedding_config: a `TPUEmbeddingConfiguration` proto describing the desired
configuration of the hardware embedding lookup tables.
job: The job (the XXX in TensorFlow device specification /job:XXX) that
contains the TPU devices that will be initialized. If job=None it is
assumed there is only one job in the TensorFlow flock, and an error will
be returned if this assumption does not hold.
Returns:
A no-op.
"""
config_string = embedding_config.SerializeToString()
with ops.device(_tpu_system_device_name(job)):
return tpu_ops.configure_tpu_embedding(config=config_string)
@tf_export(v1=["tpu.shutdown_system"])
def shutdown_system(job: Optional[Text] = None) -> ops.Operation:
"""Shuts down a running a distributed TPU system.
Args:
job: The job (the XXX in TensorFlow device specification /job:XXX) that
contains the TPU devices that will be shutdown. If job=None it is
assumed there is only one job in the TensorFlow flock, and an error will
be returned if this assumption does not hold.
"""
with ops.device(_tpu_system_device_name(job)):
shutdown_distributed_tpu = tpu_ops.shutdown_distributed_tpu()
return shutdown_distributed_tpu
@auto_control_deps.register_acd_resource_resolver
def tpu_replicated_input_resolver(
op: ops.Operation,
resource_reads: object_identity.ObjectIdentitySet,
resource_writes: object_identity.ObjectIdentitySet) -> bool:
"""Replaces TPUReplicatedInput outputs with its inputs in resource_inputs."""
# Ignore TPUReplicatedInput for ACD purposes since we will be directly adding
# control deps on the replicated inputs.
if op.type == "TPUReplicatedInput":
if resource_reads or resource_writes:
resource_reads.clear()
resource_writes.clear()
return True
else:
return False
# Replace tensors in `resource_inputs` which are outputs of TPUReplicatedInput
# with the actual replicated inputs. This allows ACD to correct add control
# deps when there are multiple calls to `run` in a
# `tf.function`.
def replace_with_unreplicated_resources(resource_inputs):
"""Replaces handles in `resource_inputs` with their unreplicated inputs."""
to_remove = []
to_add = []
for resource in resource_inputs:
if resource.op.type == "TPUReplicatedInput":
to_remove.append(resource)
to_add.extend(resource.op.inputs)
for t in to_remove:
resource_inputs.discard(t)
resource_inputs.update(to_add)
return to_add or to_remove
return bool(replace_with_unreplicated_resources(resource_reads) or
replace_with_unreplicated_resources(resource_writes))
@tf_export(v1=["tpu.PaddingSpec"])
class PaddingSpec(enum.IntEnum):
"""Represents the type of padding policies for tpu.replicate."""
# By default the policy is set to AUTO, the dynamic input shape dimension will
# be pad to maximum of all the replicas.
AUTO = 0
# Bucketize the dynamic input shape dimension into a power of 2.
POWER_OF_TWO = 1
@tf_export("tpu.XLAOptions")
class XLAOptions(
collections.namedtuple("XLAOptions", [
"use_spmd_for_xla_partitioning",
"enable_xla_dynamic_padder",
])):
"""XLA compilation options.
Attributes:
use_spmd_for_xla_partitioning: Boolean. Whether to use XLA's SPMD
partitioner instead of MPMD partitioner when compiler partitioning is
requested.
enable_xla_dynamic_padder: Boolean. Whether to enable XLA dynamic padder
infrastructure to handle dynamic shapes inputs inside XLA. True by
default. Disabling this may cause correctness issues with dynamic shapes
inputs, as XLA will just assume the inputs are with padded shapes. However
users can optionally set it to False to improve device time if masking is
already handled in the user side.
"""
def __new__(cls,
use_spmd_for_xla_partitioning=True,
enable_xla_dynamic_padder=True):
return super(XLAOptions, cls).__new__(cls, use_spmd_for_xla_partitioning,
enable_xla_dynamic_padder)
@tf_export(v1=["tpu.replicate"])
@traceback_utils.filter_traceback
def replicate(
computation: Callable[..., Any],
inputs: Optional[List[List[core_types.Tensor]]] = None,
infeed_queue: Optional[tpu_feed.InfeedQueue] = None,
device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None,
name: Optional[Text] = None,
maximum_shapes: Optional[Any] = None,
padding_spec: Optional[PaddingSpec] = None,
xla_options: Optional[XLAOptions] = None) -> List[Any]:
"""Builds a graph operator that runs a replicated TPU computation.
Example for the basic usage that `inputs` has static shape:
```python
def computation(x):
x = x + 1
return tf.math.reduce_mean(x)
x = tf.convert_to_tensor([1., 2., 3.])
y = tf.convert_to_tensor([4., 5., 6.])
tf.compat.v1.tpu.replicate(computation, inputs=[[x], [y]])
```
If the `inputs` has dynamic shapes and you would like to automatically
bucketize the inputs to avoid XLA recompilation. See the advanced example
below:
```python
def computation(x):
x = x + 1
return tf.math.reduce_mean(x)
# Assume input tensors in two replicas `x` and `y` both have dynamic shape
# ([None, 2]).
tf.compat.v1.tpu.replicate(
computation,
inputs=[x, y],
maximum_shapes=[tf.TensorShape([None, None])],
padding_spec=tf.compat.v1.tpu.PaddingSpec.POWER_OF_TWO)
```
Args:
computation: A Python function that builds the computation to replicate.
inputs: A list of lists of input tensors or `None` (equivalent to
`[[]]`), indexed by `[replica_num][input_num]`. All replicas must
have the same number of inputs. Each input can be a nested structure
containing values that are convertible to tensors. Note that passing an
N-dimension list of compatible values will result in a N-dimension list of
scalar tensors rather than a single Rank-N tensors. If you need different
behavior, convert part of inputs to tensors with `tf.convert_to_tensor`.
infeed_queue: If not `None`, the `InfeedQueue` from which to append a tuple
of arguments as inputs to computation.
device_assignment: If not `None`, a `DeviceAssignment` describing the
mapping between logical cores in the computation with physical cores in
the TPU topology. Uses a default device assignment if `None`. The
`DeviceAssignment` may be omitted if each replica of the computation uses
only one core, and there is either only one replica, or the number of
replicas is equal to the number of cores in the TPU system.
name: (Deprecated) Does nothing.
maximum_shapes: A nested structure of tf.TensorShape representing the shape
to which the respective component of each input element in each replica
should be padded. Any unknown dimensions (e.g.
tf.compat.v1.Dimension(None) in a tf.TensorShape or -1 in a tensor-like
object) will be padded to the maximum size of that dimension over all
replicas. The structure of `maximum_shapes` needs to be the same as
`inputs[0]`.
padding_spec: An enum specified by `tpu.PaddingSpec`. This describes the
padding policy when the `inputs` to `tpu.replicate` is dynamic.
One usage is to enable automatic bucketizing on the inputs by setting the
value to `tpu.PaddingSpec.POWER_OF_TWO`, which can help to reduce the
recompilation in the XLA side.
xla_options: An instance of `tpu.XLAOptions` which indicates the options
passed to XLA compiler. Use `None` for default options.
Returns:
A list of outputs, indexed by `[replica_num]` each output can be a nested
structure same as what computation() returns with a few exceptions.
Exceptions include:
1) None output: a NoOp would be returned which control-depends on
computation.
2) Single value output: A tuple containing the value would be returned.
3) Operation-only outputs: a NoOp would be returned which
control-depends on computation.
TODO(b/121383831): Investigate into removing these special cases.
Raises:
ValueError: If all replicas do not have equal numbers of input tensors.
ValueError: If the number of inputs per replica does not match
the number of formal parameters to `computation`.
ValueError: If the static `inputs` dimensions don't match with the values
given in `maximum_shapes`.
ValueError: If the structure of inputs per replica does not match
the structure of `maximum_shapes`.
"""
return split_compile_and_replicate(
computation,
inputs,
infeed_queue,
device_assignment,
name,
maximum_shapes=maximum_shapes,
padding_spec=padding_spec,
xla_options=xla_options)[1]
def _ceil_to_pow_of_n(x, n):
"""Ceil input `x` to power of `n`."""
x = math_ops.cast(x, dtypes.float32)
lognx = math_ops.log(x) / math_ops.log(n * 1.0)
lognx = math_ops.ceil(lognx)
result = math_ops.pow(n * 1.0, lognx)
result = math_ops.cast(result, dtypes.int32)
return result
def _pad_all_input(
inputs: Iterable[core_types.Tensor],
padded_shapes: List[Optional[tensor_shape.TensorShape]],
padding_spec: PaddingSpec
) -> Tuple[List[List[Any]], List[dynamic_padding.PaddingMap]]:
"""Pad all input tensors given padded_shapes.
The real shape tensors will be concatenated with the padded original inputs.
Args:
inputs: The original inputs.
padded_shapes: A list of padded shapes for each input. If an entry is None,
no padding is performed.
padding_spec: An enum specified by `tpu.PaddingSpec`. This describes the
padding policy when the `inputs` to `tf.tpu.replicate` is dynamic.
One usage is to enable automatic bucketizing on the inputs by setting the
value to `tpu.PaddingSpec.POWER_OF_TWO`, which can help to reduce the
recompilation in the XLA side.
Returns:
The padded inputs and a PaddingMap list which maps the padded input
dimension to the real shape argument index.
"""
# maximum_static_shapes[idx][i] indicates the maximum static size of ith
# dimension of the idx input among all the replicas.
maximum_static_shapes = []
# need_padding[idx][i] indicates whether the ith dimension of the idx input
# needs padding.
need_padding = []
input_shape_tensors = []
for core_idx, inputs_per_core in enumerate(inputs):
for idx, input_tensor in enumerate(inputs_per_core):
input_shape = input_tensor.get_shape().as_list()
if core_idx == 0:
input_shape_tensors.append([])
maximum_static_shapes.append(input_shape)
need_padding.append(np.full_like(input_shape, False, dtype=bool))
else:
for i, s in enumerate(input_shape):
if s is None or s != maximum_static_shapes[idx][i]:
need_padding[idx][i] = True
maximum_static_shapes[idx] = max(input_shape,
maximum_static_shapes[idx])
# Append _POST_DEVICE_REWRITE_ATTR attributes to the real shape ops.
real_input_shape = array_ops.shape(input_tensor)
real_input_shape.op._set_attr( # pylint: disable=protected-access
_POST_DEVICE_REWRITE_ATTR,
attr_value_pb2.AttrValue(b=True))
input_shape_tensors[idx].append(real_input_shape)
maximum_shapes = []
for shapes_per_input in input_shape_tensors:
maximum_shapes.append(
math_ops.reduce_max(array_ops_stack.stack(shapes_per_input), axis=0))
padded_inputs = []
real_shapes = []
padding_maps = []
for core_idx, inputs_per_core in enumerate(inputs):
padded_inputs.append([])
real_shapes.append([])
real_shape_idx = len(inputs_per_core) - 1
for idx, input_tensor in enumerate(inputs_per_core):
input_shape_tensor = input_shape_tensors[idx][core_idx]
input_shape = input_tensor.get_shape().as_list()
padded_shape = padded_shapes[idx]
# If we have no padded_shape, then skip padding.
if any(need_padding[idx]) and padded_shape is not None:
for i, s in enumerate(input_shape):
if need_padding[idx][i]:
if core_idx == 0:
real_shape_idx += 1
padding_map = dynamic_padding.PaddingMap()
padding_map.arg_index = idx
padding_map.shape_index = i
padding_map.padding_arg_index = real_shape_idx
padding_maps.append(padding_map)
real_shapes[core_idx].append(
math_ops.cast(input_shape_tensor[i], dtypes.int32))
paddings = []
for i, s in enumerate(padded_shape.dims):
if need_padding[idx][i]:
# The minimum padded dimension size is 2 as XLA doesn't support size
# 1 dynamic size.
minimum_dynamic_dim_size = 2
if s.value is not None:
# Pad to the given maximum value.
max_dim_size = max(s.value, minimum_dynamic_dim_size)
else:
# If maximum value is not given, then pad to the maximum dimension
# among all the cores.
max_dim_size = math_ops.maximum(maximum_shapes[idx][i],
minimum_dynamic_dim_size)
if padding_spec == PaddingSpec.POWER_OF_TWO:
max_dim_size = _ceil_to_pow_of_n(max_dim_size, 2)
# Pad to the given maximum value.
padding = [0, max_dim_size - input_shape_tensor[i]]
else:
padding = [0, 0]
paddings.append(padding)
if input_tensor.get_shape().is_fully_defined():
# TODO(rxsang): This is a hack to make sure padded_input has dynamic
# shapes, so any tf.size/tf.shape op performed on it won't be constant
# folded. Do we have better ways to do it?
padded_input = cond.cond(
array_ops.constant(True),
lambda: array_ops.pad(input_tensor, paddings), # pylint: disable=cell-var-from-loop
lambda: input_tensor)
else:
padded_input = array_ops.pad(input_tensor, paddings)
# Append _POST_DEVICE_REWRITE_ATTR attributes to all padded inputs.
padded_input.op._set_attr( # pylint: disable=protected-access
_POST_DEVICE_REWRITE_ATTR,
attr_value_pb2.AttrValue(b=True))
padded_inputs[core_idx].append(padded_input)
else:
padded_inputs[core_idx].append(input_tensor)
num_replicas = len(padded_inputs)
for i in range(num_replicas):
padded_inputs[i].extend(real_shapes[i])
return padded_inputs, padding_maps
def _flatten_and_filter_composite(maybe_composite, non_composite_output,
composite_output=None):
"""For an input, replaced the input by a tuple if the input is composite.
If `maybe_composite` is not composite, return the parameter
`non_composite_output` otherwise return a tuple which consists of the value of
the parameter `composite_output` the same number of times as there are
components of the composite tensor.
This is useful for computing a mask when flattening nested data with
`expand_composites=True`. For example
```python
nest.flatten(data, expand_composites=True)
```
and
```python
nest.flatten(nest.map(
data, lambda x: _flatten_and_filter_composite(x, False, True)))
```
will have the same length and second will be True if the tensor in the first
is derived from a expanding a composite tensor.
Args:
maybe_composite: A value to test for being a composite tensor.
non_composite_output: The value to return when `maybe_composite` is not a
composite.
composite_output: the value to fill the output tuple with if
`maybe_composite` is a composite.
Returns:
`non_composite_output` or a tuple with multiple copies of
`composite_output`.
"""
if isinstance(maybe_composite, composite_tensor.CompositeTensor):
num_components = len(nest.flatten(maybe_composite, expand_composites=True))
return (composite_output,) * num_components
return non_composite_output
def split_compile_and_replicate(
computation: Callable[..., Any],
inputs: Optional[List[List[core_types.Tensor]]] = None,
infeed_queue: Optional[tpu_feed.InfeedQueue] = None,
device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None,
name: Optional[Text] = None,
use_tpu: bool = True,
maximum_shapes: Optional[Any] = None,
padding_spec: Optional[PaddingSpec] = None,
xla_options: Optional[XLAOptions] = None,
) -> List[List[core_types.Tensor]]:
"""Builds graph operators that runs compilation and replicated computation.
This is a lower level interface than replicate that returns a separate compile
and execute output tensor. In the generated graph the compile op feeds into
the execute op and no additional compilation is incurred when running the
compile op before the execute op. The compile op returns additional
information about the compilation but does not return the compiled program.
Args:
computation: A Python function that builds the computation to replicate.
inputs: A list of lists of input tensors or `None` (equivalent to
`[[]]`), indexed by `[replica_num][input_num]`. All replicas must
have the same number of inputs. Each input can be a nested structure
containing values that are convertible to tensors. Note that passing an
N-dimension list of compatible values will result in a N-dimension list of
scalar tensors rather than a single Rank-N tensors. If you need different
behavior, convert part of inputs to tensors with `tf.convert_to_tensor`.
infeed_queue: If not `None`, the `InfeedQueue` from which to append a tuple
of arguments as inputs to computation.
device_assignment: If not `None`, a `DeviceAssignment` describing the
mapping between logical cores in the computation with physical cores in
the TPU topology. Uses a default device assignment if `None`. The
`DeviceAssignment` may be omitted if each replica of the computation uses
only one core, and there is either only one replica, or the number of
replicas is equal to the number of cores in the TPU system.
name: (Deprecated) Does nothing.
use_tpu: When false, the input `computation` is executed on the XLA CPU/GPU
backends. Currently, only supports a default placement (computation is
placed on GPU if one is available, and on CPU if not).
maximum_shapes: A nested structure of tf.TensorShape representing the shape
to which the respective component of each input element in each replica
should be padded. Any unknown dimensions (e.g.
tf.compat.v1.Dimension(None) in a tf.TensorShape or -1 in a tensor-like
object) will be padded to the maximum size of that dimension over all
replicas. The structure of `maximum_shapes` needs to be the same as
`inputs[0]`.
padding_spec: An enum specified by `tf.tpu.PaddingSpec`. This describes the
padding policy when the `inputs` to `tf.tpu.replicate` is dynamic.
One usage is to enable automatic bucketizing on the inputs by setting the
value to `tpu.PaddingSpec.POWER_OF_TWO`, which can help to reduce the
recompilation in the XLA side.
xla_options: An instance of `tpu.XLAOptions` which indicates the options
passed to XLA compiler. Use `None` for default options.
Returns:
A list of lists with the first list corresponding to the compile op and the
second a list of output tensors, indexed by `[replica_num][output_num]`.
Raises:
ValueError: If all replicas do not have equal numbers of input tensors.
ValueError: If the number of inputs per replica does not match
the number of formal parameters to `computation`.
ValueError: If the static `inputs` dimensions don't match with the values
given in `maximum_shapes`.
ValueError: If the structure of inputs per replica does not match
the structure of `maximum_shapes`.
"""
del name
inputs = [[]] if inputs is None else inputs
xla_options = xla_options or XLAOptions()
metadata_kwargs = {}
if device_assignment is not None:
# Turn the Numpy array into a flattened list so we can pass it as an
# operator attribute.
metadata_kwargs = {
"topology":
device_assignment.topology.serialized(),
"device_assignment":
device_assignment.core_assignment.flatten().tolist()
}
metadata_kwargs["num_cores_per_replica"] = (
device_assignment.num_cores_per_replica)
# This entry is used for enabling automatic outside compilation.
metadata_kwargs["allow_soft_placement"] = config.get_soft_device_placement()
if config.get_soft_device_placement():
logging.info("Automatic outside compilation is enabled. "
"Ops without XLA kernels will be automatically "
"placed on CPU.")
if not isinstance(inputs, list):
raise TypeError("tpu.replicate() inputs must be a list of lists/tuples, "
f"received {type(inputs)}")
if any(not isinstance(inp, (list, tuple)) for inp in inputs):
raise TypeError(
"tpu.replicate() inputs must be a list of lists/tuples, "
f"received types: {[type(inp) for inp in inputs]}")
num_replicas = len(inputs)
# No replicas? Nothing to do.
if num_replicas == 0:
return []
# Checks all replicas have the same structure.
for i in range(1, num_replicas):
nest.assert_same_structure(inputs[0], inputs[i])
# Explicitly read variables.
inputs = variable_utils.convert_variables_to_tensors(inputs)
# Flatten inputs. This structure may contain None values, which will be
# handled later.
flat_inputs_with_nones = [
nest.flatten(per_replica_input, expand_composites=True)
for per_replica_input in inputs
]
# Mask parallel to one replica's inputs with True for tensors coming from
# composites.
is_composite = nest.flatten(nest.map_structure(
lambda x: _flatten_and_filter_composite(x, False, True), inputs[0]))
# Converts inputs to Tensors, replacing Nones with a placeholder 0 since
# tpu_ops.tpu_replicated_input() can't handle non-Tensor values.
flat_inputs = []
for inp in flat_inputs_with_nones:
flat_inputs.append([
constant_op.constant(0) if x is None else ops.convert_to_tensor(x)
for x in inp
])
# Verifies that all replicas have matching numbers and types of inputs
flat_input_types = [x.dtype for x in flat_inputs[0]]
input_arity = len(inputs[0])
flat_input_arity = len(flat_input_types)
for i in range(num_replicas):
if len(inputs[i]) != input_arity:
raise ValueError("Replicas must have the same number of inputs. "
"Replica 0 had {} inputs, replica {} had {} "
"inputs.".format(input_arity, i, len(inputs[i])))
types = [x.dtype for x in flat_inputs[i]]
if types != flat_input_types:
raise ValueError("Replicas must have matching input types. Replica 0 had "
"input types {}, replica {} had input types {}".format(
flat_input_types, i, types))
arg_error = xla.check_function_argument_count(
computation, input_arity, infeed_queue)
if arg_error is not None:
if infeed_queue is None:
raise TypeError(
"Supplied computation cannot be called with the specified inputs. "
f"You specified {input_arity} inputs: {[i.name for i in inputs[0]]}, "
f"but the computation needs {arg_error}")
else:
raise TypeError(
"Supplied computation cannot be called with the specified inputs. "
f"You specified {input_arity} inputs: {[i.name for i in inputs[0]]} ",
f"and {infeed_queue.number_of_tuple_elements} additional inputs "
f"from infeed, but the computation needs {arg_error}")
dynamic_shape_inputs = False
if maximum_shapes:
if infeed_queue:
raise ValueError(
"Dynamic input shapes are not supported with infeed queues")
# Make sure maximum_shapes has the same structure as inputs.
nest.assert_same_structure(inputs[0], maximum_shapes, check_types=False)
# Flatten padded shapes:
# For composite tensor components, we don't want to pad them. For each
# entry of maximum_shapes that corresponds to a composite tensor, replace it
# by a tuple of Nones of the same length as the number of components of the
# composite tensor. When we flatten a second time, this makes
# flat_maximum_shapes have the same length as flat_inputs[i]. We can then
# avoid padding these tensors. The assumption is that they will be used by
# outside compilation or that the components are statically shaped and will
# be used by tpu compatible ops.
flat_maximum_shapes = nest.flatten(
[_flatten_and_filter_composite(x, y)
for x, y in zip(nest.flatten(inputs[0]),
nest.flatten(maximum_shapes))])
flat_maximum_shapes = [
tensor_shape.TensorShape(s) if s is not None else None
for s in flat_maximum_shapes
]
nest.assert_same_structure(flat_inputs[0], flat_maximum_shapes,
check_types=False)
unpadded_inputs = flat_inputs
flat_inputs, padding_maps = _pad_all_input(unpadded_inputs,
flat_maximum_shapes,
padding_spec)
if padding_maps:
dynamic_shape_inputs = True
logging.info("TPU has inputs with dynamic shapes: %s", inputs[0])
metadata_kwargs["step_marker_location"] = getattr(
computation, "step_marker_location", "STEP_MARK_AT_ENTRY")
metadata_kwargs["use_spmd_for_xla_partitioning"] = \
xla_options.use_spmd_for_xla_partitioning
graph = ops.get_default_graph()
# Fan-in: Builds a TPUReplicatedInput node for each input.
flat_replicated_inputs = []
for i in range(0, len(flat_inputs[0])):
replicas = [flat_inputs[replica][i] for replica in range(num_replicas)]
flat_replicated_inputs.append(
tpu_ops.tpu_replicated_input(
replicas, name="input{}".format(i)))
if isinstance(graph, func_graph.FuncGraph):
# When we are in Tensorflow 2.0 function, 'graph' will be a FuncGraph
# object. If both outside graph and this function have a TPU cluster,
# they will have the same cluster name and it will cause problems (because
# we lower functional ops in Tensorflow 2.0). Append function name to
# 'cluster_name' to avoid cluster name collision.
cluster_name = graph.unique_name("cluster_" + graph.name)
else:
cluster_name = graph.unique_name("cluster")
pivot = control_flow_ops.no_op(name=cluster_name + "/pivot")
pivot._set_attr(_PIVOT_FOR_CLUSTER, # pylint: disable=protected-access
attr_value_pb2.AttrValue(s=compat.as_bytes(cluster_name)))
context = tpu_replication.TPUReplicateContext(
name=cluster_name, num_replicas=num_replicas, pivot=pivot)
try:
context.Enter()
metadata = tpu_ops.tpu_replicate_metadata(
num_replicas=num_replicas, use_tpu=use_tpu, **metadata_kwargs)
with tpu_function.tpu_shard_context(
num_replicas), ops.control_dependencies([metadata]):
if dynamic_shape_inputs and xla_options.enable_xla_dynamic_padder:
for padding_map in padding_maps:
input_shape = flat_replicated_inputs[padding_map.arg_index].shape
flat_replicated_inputs[
padding_map.arg_index] = tf2xla.set_dynamic_dimension_size(
flat_replicated_inputs[padding_map.arg_index],
padding_map.shape_index,
flat_replicated_inputs[padding_map.padding_arg_index])
flat_replicated_inputs[padding_map.arg_index].set_shape(input_shape)
# Add identity ops so even unused inputs are "consumed" by the
# computation. This is to avoid orphaned TPUReplicatedInput nodes.
# TODO(phawkins): consider instead pruning unused TPUReplicatedInput
# and eliding trivial TPUReplicatedInput/TPUReplicatedOutput pairs.
flat_replicated_inputs = [
array_ops.identity(x, name="replicated_input_{}".format(i))
for i, x in enumerate(flat_replicated_inputs)
]
for i, composite in zip(flat_replicated_inputs, is_composite):
# pylint: disable=protected-access
# Add an attribute to the identity node so that they could be removed in
# encapsulate TPU computation pass if unused. However we don't remove
# inputs when dynamic padding is enabled.
# TODO(rxsang): Use other ways except argument index in padding_map so
# outside compilation can work with dynamic padding correctly.
if not dynamic_shape_inputs or composite:
i.op._set_attr("_tpu_input_identity",
attr_value_pb2.AttrValue(b=True))
# pylint: enable=protected-access
# Clobber replicated placeholders with Nones.
computation_inputs = [
None if inp is None else replicated for replicated, inp in zip(
flat_replicated_inputs, flat_inputs_with_nones[0])
]
# Unflatten the computation inputs to match original input structure.
computation_inputs = nest.pack_sequence_as(
structure=inputs[0],
flat_sequence=computation_inputs[:flat_input_arity],
expand_composites=True)
# If there is an infeed queue, adds the dequeued values to the
# computation's inputs.
if infeed_queue is not None:
infeed_queue.set_number_of_shards(num_replicas)
for t in infeed_queue.generate_dequeue_op():
computation_inputs.append(t)
# Only resource variables work inside a TPU computation, so turn on
# resource variables for the computation.
# TODO(phawkins): consider removing this code. It will
# be less confusing to clients if they knowingly choose to use resource
# variables.
# Partitioned variables is not supported (b/112311320).
vscope = variable_scope.get_variable_scope()
saved_use_resource = vscope.use_resource
saved_custom_getter = vscope.custom_getter
def custom_getter(getter, name, *args, **kwargs):
"""Variables on TPU have a few restrictions."""
partitioner = kwargs.get("partitioner", None)
if partitioner is not None:
kwargs["partitioner"] = None
logging.warning(
"Partitioned variables are not supported on TPU. Got "
"`partitioner` that is %s for variable %s. "
"Setting `partitioner` to `None`.", partitioner, name)
if saved_custom_getter is None:
return getter(name, *args, **kwargs)
else:
return saved_custom_getter(getter, name, *args, **kwargs)
vscope.set_use_resource(True)
vscope.set_custom_getter(custom_getter)
outputs = computation(*computation_inputs)
vscope.set_use_resource(saved_use_resource)
vscope.set_custom_getter(saved_custom_getter)
outputs = variable_utils.convert_variables_to_tensors(outputs)
need_spmd_partitioning = (
xla_options.use_spmd_for_xla_partitioning and
device_assignment is not None and
device_assignment.num_cores_per_replica > 1)
outputs_is_flat = xla.is_flat(outputs)
if outputs_is_flat:
output_tensors, control_deps, pack_template = _postprocess_flat_outputs(
outputs, need_spmd_partitioning)
else:
output_tensors, control_deps, pack_template = (
_postprocess_non_flat_outputs(outputs, need_spmd_partitioning))
if tensor_tracer.TensorTracer.is_enabled():
if tf2.enabled():
logging.warn("TF API ver >= 2.0 detected. "
"Tensor Tracer v1 is not enabled.")
else:
tt = tensor_tracer.TensorTracer()
output_tensors = tt.trace_tpu(ops.get_default_graph(),
output_tensors, control_deps,
num_replicas)
context.ExitResult(output_tensors)
finally:
context.report_unsupported_operations()
context.Exit()
host_compute_core = context.HostComputeCore()
if host_compute_core:
attr_value = attr_value_pb2.AttrValue()
attr_value.list.s.extend(compat.as_bytes(x) for x in host_compute_core)
metadata._set_attr("host_compute_core", attr_value) # pylint: disable=protected-access
with ops.control_dependencies([metadata]):
if use_tpu:
compile_status = tpu_ops.tpu_compilation_result()
op = compile_status.op
attr_value = attr_value_pb2.AttrValue(s=compat.as_bytes(cluster_name))
op._set_attr(_TPU_COMPILATION_STATUS_ATTR, attr_value) # pylint: disable=protected-access
else:
compile_status = control_flow_ops.no_op(name="compilation_status")
if not output_tensors:
# Returns a list of NoOps dependent on the replication Op, indexed by
# [replica_num].
return [
compile_status,
[
control_flow_ops.group(control_deps, name="shard_%d" % i)
for i in range(num_replicas)
]
]
# Fan-out: Builds a TPUReplicatedOutput node for each output.
replicated_outputs = [[] for i in range(num_replicas)]
for i, t in enumerate(output_tensors):
# None values returned by the computation can't be sent to
# tpu_ops.tpu_replicated_output(), we handle them specially here. We can
# avoid the placeholder 0 routine required on the inputs since outputs are
# replicated per-tensor, not per-replica, so we can skip replication.
if t is None:
for replica in range(num_replicas):
replicated_outputs[replica].append(None)
continue
# Fan-out: Builds a TPUReplicatedOutput node for each output.
ys = tpu_ops.tpu_replicated_output(
t, num_replicas, name="output{}".format(i))
# Wraps the outputs in identity operators so the names of any possible
# `fetch` nodes are preserved by the replication rewrite.
with ops.control_dependencies(control_deps):
for replica in range(num_replicas):
replicated_outputs[replica].append(
array_ops.identity(
ys[replica], name="output_%d_shard_%d" % (i, replica)))
replicated_outputs = [
nest.pack_sequence_as(pack_template, replica_outs, expand_composites=True)
for replica_outs in replicated_outputs
]
return [compile_status, replicated_outputs]
def _postprocess_flat_outputs(
outputs: Any,
need_spmd_partitioning: bool
) -> Tuple[List[Optional[core_types.Tensor]], List[ops.Operation], List[Any]]:
"""Validates non-flat outputs, add backs device assignments and other attrs.
Args:
outputs: Output from `computation` inside `tpu.rewrite`.
need_spmd_partitioning: Whether XLA SPMD partitioning is needed.
Returns:
- Tensors extracted from outputs.
- Operations extracted from outputs.
- A pack template for use with nest.pack_sequence_as to pack the tensors.
"""
# Following code segment is to preserve legacy behavior. Previously we only
# supported flat outputs and thus for consistency it was nice to convert even
# single element into a tuple. But now that we support arbitrary output
# structure, this is no longer necessary.
# TODO(b/121383831): Migrate all legacy use cases and delete this special
# case.
# If the computation returns `None`, make it an empty tuple.
if outputs is None:
outputs = tuple()
# For legacy / backwards compatibility reasons we return a list for "flat"
# output values (even if the user's flat return value was a different type or
# even just a scalar value) so use nest.flatten to compute a flat list pack
# template.
pack_template = nest.flatten(outputs, expand_composites=False)
# Even though outputs is already "flat", we flatten any composites so their
# component tensors can be tagged and replicated. The pack_template will be
# used by the caller to repack the composite tensors.
outputs = nest.flatten(outputs, expand_composites=True)
# Append `no_op` here so that fetching any return value of this function
# will trigger TPUExecute node.
outputs += (control_flow_ops.no_op(),)
maybe_convert = lambda x: None if x is None else ops.convert_to_tensor(x)
try:
if need_spmd_partitioning:
outputs = [
o if isinstance(o, ops.Operation) else maybe_convert(o)
for o in outputs
]
else:
with ops.device(core(0)):
outputs = [
o if isinstance(o, ops.Operation) else maybe_convert(o)
for o in outputs
]
except Exception as e:
raise ValueError(
"TPU function return values must all either be Operations or "
f"convertible to Tensors. Got error: {e}")
# Separates the returned Operations and Tensors.
output_operations = [o for o in outputs if isinstance(o, ops.Operation)]
output_tensors = [o for o in outputs if not isinstance(o, ops.Operation)]
if outputs != output_tensors + output_operations:
raise ValueError(
"TPU functions must return zero-or more Tensor values followed by "
"zero or more Operations.")
# Trim operations off the end of the pack template. output_operations has 1
# extra element due to the no-op that is added.
if len(output_operations) > 1:
pack_template = pack_template[:1 - len(output_operations)]
# Wraps outputs in Identity ops. Otherwise a replicated input copied
# straight to an output would bypass the replicate(). This would be bad
# because the TPUReplicatedInput/TPUReplicatedOutput operator would not
# be rewritten away, leading to a runtime error.
# TODO(phawkins): extend the rewrite to elide these nodes instead.
new_output_tensors = []
for t in output_tensors:
if t is None:
new_output_tensors.append(None)
elif need_spmd_partitioning:
o = array_ops.identity(t)
# pylint: disable=protected-access
o.op._set_attr("_tpu_output_identity", attr_value_pb2.AttrValue(b=True))
# pylint: enable=protected-access
new_output_tensors.append(o)
else:
with ops.device(t.device if t.device else core(0)):
o = array_ops.identity(t)
# pylint: disable=protected-access
o.op._set_attr("_tpu_output_identity", attr_value_pb2.AttrValue(b=True))
# pylint: enable=protected-access
new_output_tensors.append(o)
return new_output_tensors, output_operations, pack_template
def _postprocess_non_flat_outputs(
outputs: Any,
need_spmd_partitioning: bool
) -> Tuple[List[Optional[core_types.Tensor]], List[ops.Operation], List[Any]]:
"""Validates non-flat outputs, add backs device assignments and other attrs.
Args:
outputs: Output from `computation` inside `tpu.rewrite`.
need_spmd_partitioning: Whether XLA SPMD partitioning is needed.
Returns:
- Tensors extracted from outputs.
- An empty Operations list because Operations are not allowed in non-flat
outputs.
- A pack template for use with nest.pack_sequence_as to pack the tensors.
"""
# Flatten output items.
flat_outputs = nest.flatten(outputs, expand_composites=True)
# Convert all non-None non-Operation outputs to Tensors.
for i, o in enumerate(flat_outputs):
if o is None:
flat_outputs[i] = None
continue
if isinstance(o, ops.Operation):
raise ValueError(
"tpu.rewrite does not support Operation as return value in non-flat "
"output structure. You can set returned Operations as control "
"dependencies of returned Tensors so Operations are triggered when "
f'Tensors are evaluated. Operation found: "{o.name}"')
try:
o = ops.convert_to_tensor(o)
except Exception as e:
raise ValueError(
"TPU function return values must all either be Operations or "
f'convertible to Tensors. Got error: "{e}"')
# Wraps outputs in Identity ops. Otherwise a replicated input copied
# straight to an output would bypass the replicate(). This would be bad
# because the TPUReplicatedInput/TPUReplicatedOutput operator would not
# be rewritten away, leading to a runtime error.
# TODO(phawkins): extend the rewrite to elide these nodes instead.
if need_spmd_partitioning:
o = array_ops.identity(o)
# pylint: disable=protected-access
o.op._set_attr("_tpu_output_identity", attr_value_pb2.AttrValue(b=True))
# pylint: enable=protected-access
flat_outputs[i] = array_ops.identity(o)
else:
with ops.device(o.device if o.device else core(0)):
o = array_ops.identity(o)
# pylint: disable=protected-access
o.op._set_attr("_tpu_output_identity", attr_value_pb2.AttrValue(b=True))
# pylint: enable=protected-access
flat_outputs[i] = array_ops.identity(o)
# All flat_outputs are Tensors, and no Operations.
return flat_outputs, [], outputs
def split_compile_and_shard(
computation: Callable[..., Any],
inputs: Optional[List[List[Optional[core_types.Tensor]]]] = None,
num_shards: int = 1,
input_shard_axes: Optional[List[int]] = None,
outputs_from_all_shards: Union[bool, List[bool]] = True,
output_shard_axes: Optional[List[int]] = None,
infeed_queue: Optional[tpu_feed.InfeedQueue] = None,
device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None,
name: Optional[Text] = None,
xla_options: Optional[XLAOptions] = None,
) -> Tuple[ops.Operation, List[core_types.Tensor]]:
"""Shards `computation` for parallel execution.
`inputs` must be a list of Tensors or None (equivalent to an empty list), each
of which has a corresponding split axis (from `input_shard_axes`). Each input
is split into `num_shards` pieces along the corresponding axis, and
computation is applied to each shard in parallel.
Tensors are broadcast to all shards if they are lexically captured by
`computation`. e.g.,
x = tf.constant(7)
def computation():
return x + 3
... = shard(computation, ...)
If `outputs_from_all_shards` is true, the outputs from all shards of
`computation` are concatenated back together along their `output_shard_axes`.
Otherwise, each output is taken from an arbitrary shard.
Inputs and outputs of the computation must be at least rank-1 Tensors.
Args:
computation: A Python function that builds a computation to apply to each
shard of the input.
inputs: A list of input tensors or None (equivalent to an empty list). Each
input tensor has a corresponding shard axes, given by `input_shard_axes`,
which must have size divisible by `num_shards`.
num_shards: The number of shards.
input_shard_axes: A list of dimensions along which to shard `inputs`, or
`None`. `None` means "shard all inputs along dimension 0". If not `None`,
there must be one dimension per input.
outputs_from_all_shards: Boolean or list of boolean. For each output, if
`True`, outputs from all shards are concatenated along the corresponding
`output_shard_axes` entry. Otherwise, each output is taken
from an arbitrary shard. If the argument is a boolean, the argument's
value is used for each output.
output_shard_axes: A list of dimensions along which to concatenate the
outputs of `computation`, or `None`. `None` means "concatenate all outputs
along dimension 0". If not `None`, there must be one dimension per output.
Ignored if `outputs_from_all_shards` is False.
infeed_queue: If not `None`, the `InfeedQueue` to use to augment the inputs
of `computation`.
device_assignment: If not `None`, a `DeviceAssignment` describing the
mapping between logical cores in the computation with physical cores in
the TPU topology. Uses a default device assignment if `None`. The
`DeviceAssignment` may be omitted if each shard of the computation uses
only one core, and there is either only one shard, or the number of shards
is equal to the number of cores in the TPU system.
name: (Deprecated) Does nothing.
xla_options: An instance of `tpu.XLAOptions` which indicates the options
passed to XLA compiler. Use `None` for default options.
Returns:
A tuple of (compile op, [output tensors]).
Raises:
ValueError: If num_shards <= 0
ValueError: If len(input_shard_axes) != len(inputs)
ValueError: If len(output_shard_axes) != len(outputs from `computation`)
"""
# TODO(phawkins): consider adding support for broadcasting Tensors passed as
# inputs.
if num_shards <= 0:
raise ValueError(
f"num_shards must be a positive integer. Received {num_shards}")
inputs = [] if inputs is None else inputs
if not isinstance(inputs, list):
raise TypeError("tpu.shard()'s inputs must be a list of Tensors or None. "
f"Received {type(inputs)}")
# Converts inputs to Tensors.
inputs = [ops.convert_to_tensor(x) for x in inputs]
if input_shard_axes is None:
input_shard_axes = [0] * len(inputs)
if len(inputs) != len(input_shard_axes):
raise ValueError("Length of input_shard_axes must be equal to the number "
f"of inputs. Received {len(inputs)} inputs and "
f"{len(input_shard_axes)} input_shard_axes.")
if inputs:
# Splits the `inputs` along the corresponding `input_shard_axes`, giving
# lists with layout [input][shard]
split_inputs = [
array_ops.split(x, num_shards, axis=axis)
for (axis, x) in zip(input_shard_axes, inputs)]
# Transposes the input lists to have layout [shard][input]
transposed_inputs = [list(i) for i in zip(*split_inputs)]
else:
transposed_inputs = [[]] * num_shards
compile_op, outputs = split_compile_and_replicate(
computation,
transposed_inputs,
infeed_queue=infeed_queue,
device_assignment=device_assignment,
name=name,
xla_options=xla_options)
# There must be at least one shard since num_shards > 0.
# TODO(b/36647078) remove disable when pylint bug is fixed.
# pylint: disable=indexing-exception
if isinstance(outputs[0], ops.Operation):
# pylint: enable=indexing-exception
# There were no outputs from the computation and replicate returned a list
# of NoOps with control dependencies on the computation. Return the first
# one so it can be used as a control dependency or fetch node.
# TODO(b/36647078) remove disable when pylint bug is fixed.
# pylint: disable=indexing-exception
return compile_op, [outputs[0]]
# pylint: enable=indexing-exception
# TODO(b/36647078) remove disable when pylint bug is fixed.
# pylint: disable=indexing-exception
num_outputs = len(outputs[0])
# pylint: enable=indexing-exception
if output_shard_axes is None:
output_shard_axes = [0] * num_outputs
if num_outputs != len(output_shard_axes):
raise ValueError("Length of output_shard_axes must be equal to the number "
f"of outputs. Received {num_outputs} outputs "
f"and {len(output_shard_axes)} output_shard_axes.")
if isinstance(outputs_from_all_shards, bool):
outputs_from_all_shards = [outputs_from_all_shards] * num_outputs
if num_outputs != len(outputs_from_all_shards):
raise ValueError(
"Length of outputs_from_all_shards must be equal to the number of "
f"outputs. Received {num_outputs} outputs and "
f"{len(outputs_from_all_shards)} outputs_from_all_shards.")
results = []
for (axis, all_shards, x) in zip(output_shard_axes, outputs_from_all_shards,
zip(*outputs)):
if all_shards:
# Concatenate all of the outputs together (use stack for scalars).
shape = x[0].shape
is_scalar = shape is not None and (shape.ndims == 0)
results.append((array_ops_stack.stack(list(x)) if is_scalar
else array_ops.concat(list(x), axis=axis)))
else:
# TODO(phawkins): use a smarter policy, e.g., round-robin across shards.
results.append(x[0])
return compile_op, results
@tf_export(v1=["tpu.shard"])
@traceback_utils.filter_traceback
def shard(
computation: Callable[..., Any],
inputs: Optional[List[core_types.Tensor]] = None,
num_shards: int = 1,
input_shard_axes: Optional[List[int]] = None,
outputs_from_all_shards: Union[bool, List[bool]] = True,
output_shard_axes: Optional[List[int]] = None,
infeed_queue: Optional[tpu_feed.InfeedQueue] = None,
device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None,
name: Optional[Text] = None,
xla_options: Optional[XLAOptions] = None) -> List[core_types.Tensor]:
"""Shards `computation` for parallel execution.
`inputs` must be a list of Tensors or None (equivalent to an empty list), each
of which has a corresponding split axis (from `input_shard_axes`). Each input
is split into `num_shards` pieces along the corresponding axis, and
computation is applied to each shard in parallel.
Tensors are broadcast to all shards if they are lexically captured by
`computation`. e.g.,
x = tf.constant(7)
def computation():
return x + 3
... = shard(computation, ...)
TODO(phawkins): consider adding support for broadcasting Tensors passed
as inputs.
If `outputs_from_all_shards` is true, the outputs from all shards of
`computation` are concatenated back together along their `output_shard_axes`.
Otherwise, each output is taken from an arbitrary shard.
Inputs and outputs of the computation must be at least rank-1 Tensors.
Args:
computation: A Python function that builds a computation to apply to each
shard of the input.
inputs: A list of input tensors or None (equivalent to an empty list). Each
input tensor has a corresponding shard axes, given by `input_shard_axes`,
which must have size divisible by `num_shards`.
num_shards: The number of shards.
input_shard_axes: A list of dimensions along which to shard `inputs`, or
`None`. `None` means "shard all inputs along dimension 0". If not `None`,
there must be one dimension per input.
outputs_from_all_shards: Boolean or list of boolean. For each output, if
`True`, outputs from all shards are concatenated along the corresponding
`output_shard_axes` entry. Otherwise, each output is taken
from an arbitrary shard. If the argument is a boolean, the argument's
value is used for each output.
output_shard_axes: A list of dimensions along which to concatenate the
outputs of `computation`, or `None`. `None` means "concatenate all outputs
along dimension 0". If not `None`, there must be one dimension per output.
Ignored if `outputs_from_all_shards` is False.
infeed_queue: If not `None`, the `InfeedQueue` to use to augment the inputs
of `computation`.
device_assignment: If not `None`, a `DeviceAssignment` describing the
mapping between logical cores in the computation with physical cores in
the TPU topology. Uses a default device assignment if `None`. The
`DeviceAssignment` may be omitted if each shard of the computation uses
only one core, and there is either only one shard, or the number of shards
is equal to the number of cores in the TPU system.
name: (Deprecated) Does nothing.
xla_options: An instance of `tpu.XLAOptions` which indicates the options
passed to XLA compiler. Use `None` for default options.
Returns:
A list of output tensors.
Raises:
ValueError: If num_shards <= 0
ValueError: If len(input_shard_axes) != len(inputs)
ValueError: If len(output_shard_axes) != len(outputs from `computation`)
"""
return split_compile_and_shard(
computation,
inputs=inputs,
num_shards=num_shards,
input_shard_axes=input_shard_axes,
outputs_from_all_shards=outputs_from_all_shards,
output_shard_axes=output_shard_axes,
infeed_queue=infeed_queue,
device_assignment=device_assignment,
name=name,
xla_options=xla_options)[1]
@tf_export(v1=["tpu.batch_parallel"])
@traceback_utils.filter_traceback
def batch_parallel(
computation: Callable[..., Any],
inputs: Optional[List[List[Optional[core_types.Tensor]]]] = None,
num_shards: int = 1,
infeed_queue: Optional[tpu_feed.InfeedQueue] = None,
device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None,
name: Optional[Text] = None,
xla_options: Optional[XLAOptions] = None):
"""Shards `computation` along the batch dimension for parallel execution.
Convenience wrapper around shard().
`inputs` must be a list of Tensors or None (equivalent to an empty list).
Each input is split into `num_shards` pieces along the 0-th dimension, and
computation is applied to each shard in parallel.
Tensors are broadcast to all shards if they are lexically captured by
`computation`. e.g.,
x = tf.constant(7)
def computation():
return x + 3
... = shard(computation, ...)
The outputs from all shards are concatenated back together along their 0-th
dimension.
Inputs and outputs of the computation must be at least rank-1 Tensors.
Args:
computation: A Python function that builds a computation to apply to each
shard of the input.
inputs: A list of input tensors or None (equivalent to an empty list). The
0-th dimension of each Tensor must have size divisible by `num_shards`.
num_shards: The number of shards.
infeed_queue: If not `None`, the `InfeedQueue` from which to append a tuple
of arguments as inputs to `computation`.
device_assignment: If not `None`, a `DeviceAssignment` describing the
mapping between logical cores in the computation with physical cores in
the TPU topology. Uses a default device assignment if `None`. The
`DeviceAssignment` may be omitted if each shard of the computation uses
only one core, and there is either only one shard, or the number of shards
is equal to the number of cores in the TPU system.
name: (Deprecated) Does nothing.
xla_options: An instance of `tpu.XLAOptions` which indicates the options
passed to XLA compiler. Use `None` for default options.
Returns:
A list of output tensors.
Raises:
ValueError: If `num_shards <= 0`
"""
return shard(
computation,
inputs,
num_shards=num_shards,
infeed_queue=infeed_queue,
device_assignment=device_assignment,
name=name,
xla_options=xla_options)
@tf_export(v1=["tpu.rewrite"])
@traceback_utils.filter_traceback
def rewrite(
computation: Callable[..., Any],
inputs: Optional[List[List[Optional[core_types.Tensor]]]] = None,
infeed_queue: Optional[tpu_feed.InfeedQueue] = None,
device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None,
name: Optional[Text] = None,
xla_options: Optional[XLAOptions] = None) -> Any:
"""Rewrites `computation` for execution on a TPU system.
Args:
computation: A Python function that builds a computation to apply to the
input. If the function takes n inputs, 'inputs' should be a list of n
tensors.
`computation` may return a list of operations and tensors. Tensors must
come before operations in the returned list. The return value of
`rewrite` is a list of tensors corresponding to the tensors from the
output of `computation`.
All `Operation`s constructed during `computation` will be executed when
evaluating any of the returned output tensors, not just the ones returned.
inputs: A list of input tensors or `None` (equivalent to an empty list).
Each input can be a nested structure containing values that are
convertible to tensors. Note that passing an N-dimension list of
compatible values will result in a N-dimension list of scalar tensors
rather than a single Rank-N tensors. If you need different behavior,
convert part of inputs to tensors with `tf.convert_to_tensor`.
infeed_queue: If not `None`, the `InfeedQueue` from which to append a tuple
of arguments as inputs to `computation`.
device_assignment: if not `None`, a `DeviceAssignment` describing the
mapping between logical cores in the computation with physical cores in
the TPU topology. May be omitted for a single-core computation, in which
case the core attached to task 0, TPU device 0 is used.
name: (Deprecated) Does nothing.
xla_options: An instance of `tpu.XLAOptions` which indicates the options
passed to XLA compiler. Use `None` for default options.
Returns:
Same data structure as if computation(*inputs) is called directly with some
exceptions for correctness. Exceptions include:
1) None output: a NoOp would be returned which control-depends on
computation.
2) Single value output: A tuple containing the value would be returned.
3) Operation-only outputs: a NoOp would be returned which
control-depends on computation.
TODO(b/121383831): Investigate into removing these special cases.
"""
# TODO(b/36647078) remove disable when pylint bug is fixed.
# pylint: disable=indexing-exception
return replicate(
computation,
None if inputs is None else [inputs],
infeed_queue=infeed_queue,
device_assignment=device_assignment,
name=name,
xla_options=xla_options)[0]
# pylint: enable=indexing-exception
# Operations that indicate some error in the user's inference graph.
_DENYLISTED_INFERENCE_OPS = set([
"ReadVariableOp",
"AssignVariableOp",
"AssignAddVariableOp",
"AssignSubVariableOp",
"VarHandleOp",
"Variable",
"VariableV2",
])
def under_tpu_inference_context() -> bool:
"""Check if it is currently under `_TPUInferenceContext`."""
graph = ops.get_default_graph()
while graph:
context = graph._get_control_flow_context() # pylint: disable=protected-access
while context:
if isinstance(context, _TPUInferenceContext):
return True
context = context.outer_context
if isinstance(graph, function._FuncGraph): # pylint: disable=protected-access
graph = graph._outer_graph # pylint: disable=protected-access
elif isinstance(graph, func_graph.FuncGraph):
graph = graph.outer_graph
else:
return False
class _TPUInferenceContext(control_flow_ops.XLAControlFlowContext):
"""A `ControlFlowContext` for nodes inside a TPU inference computation.
The primary role of `_TPUInferenceContext` is to indicate the mode of
operation and possibly sanity check operators inside a
tpu.rewrite_for_inference() computation.
"""
def __init__(self, name: Text, check_ops: bool = True):
super(_TPUInferenceContext, self).__init__()
self._name = name
self._check_ops = check_ops
def AddOp(self, op):
self._AddOpInternal(op)
def _AddOpInternal(self, op):
# pylint: disable=protected-access
if self._check_ops and op.type in _DENYLISTED_INFERENCE_OPS:
raise NotImplementedError(
f"Operation of type {op.type} ({op.name}) is not supported on the "
"TPU for inference. Execution will fail if this op is used in the "
"graph. Make sure your variables are using variable_scope.")
if self._outer_context:
self._outer_context.AddInnerOp(op)
def AddValue(self, val):
result = val
if self._outer_context:
result = self._outer_context.AddValue(val)
return result
def AddInnerOp(self, op):
self._AddOpInternal(op)
@property
def grad_state(self):
return None
def validate_inference_rewrite_for_variables(graph: ops.Graph):
"""Validates whether rewrite_for_inference() 'worked' for variables.
The rewrite_for_inference() method is supposed to append GuaranteeConstOps
after ReadVariableOps, but this mechanism works only if you are using
tf.compat.v1.get_variable() to create and access variables in your tpu
computation. This validation method can be called immediately after calling
tpu.rewrite_for_inference() to check whether GuaranteeConstOps where added
to the graph.
Typical usages:
tpu.validate_inference_rewrite_for_variables(
tf.compat.v1.get_default_graph())
tpu.validate_inference_rewrite_for_variables(sess.graph)
Args:
graph: The graph which needs to be validated.
Raises:
RuntimeError: if validation failed.
"""
if not any(x.type == "GuaranteeConst" for x in graph.get_operations()):
raise RuntimeError(
"No GuaranteeConst ops found in the graph after running "
"tpu.rewrite_for_inference(...). Please check that you are using "
"tf.get_variable() to create and access variables in your tpu "
"computation.")
def rewrite_for_inference(
computation: Callable[..., Any],
inputs: Optional[List[core_types.Tensor]] = None,
infeed_queue: Optional[tpu_feed.InfeedQueue] = None,
device_assignment: Optional[device_assignment_lib.DeviceAssignment] = None,
name: Optional[Text] = None) -> List[core_types.Tensor]:
"""Rewrites `computation` for inference on a TPU system.
Other than 'rewriting' the computation to run on a TPU, if using variables
in your computation, it moves the ReadVariableOps outside the TPU
computation, and adds GuaranteeConst ops just after the ReadVariableOps.
This mechanism works only if you are using tf.compat.v1.get_variable() to
create and access variables in your tpu computation. You can validate
whether this worked, by calling validate_inference_rewrite_for_variables()
method immediately after this method to check whether GuaranteeConstOps
where added to the graph.
Args:
computation: A Python function that builds a computation to apply to the
input. If the function takes n inputs, 'inputs' should be a list of n
tensors. If the function returns m outputs, rewrite will return a list of
m tensors.
inputs: A list of input tensors or `None` (equivalent to an empty list).
infeed_queue: If not `None`, the `InfeedQueue` from which to append a tuple
of arguments as inputs to `computation`.
device_assignment: if not `None`, a `DeviceAssignment` describing the
mapping between logical cores in the computation with physical cores in
the TPU topology. May be omitted for a single-core computation, in which
case the core attached to task 0, TPU device 0 is used.
name: The name of the operator.
Returns:
A list of output tensors.
"""
def guarantee_const_getter(getter, name, *args, **kwargs):
with ops.control_dependencies(None):
return array_ops.guarantee_const(
getter(name, *args, **kwargs), name=name + "/GuaranteeConst")
def wrapped_computation(*args, **kwargs):
"""Execute computation under `_TPUInferenceContext`."""
context = _TPUInferenceContext(
name=ops.get_default_graph().unique_name("rewrite_for_inference"))
try:
context.Enter()
vscope = variable_scope.get_variable_scope()
prev_custom_getter = vscope.custom_getter
prev_caching_device = vscope.caching_device
vscope.set_custom_getter(guarantee_const_getter)
vscope.set_caching_device(lambda op: op.device)
result = computation(*args, **kwargs)
vscope.set_custom_getter(prev_custom_getter)
vscope.set_caching_device(prev_caching_device)
finally:
context.Exit()
return result
# pylint: disable=undefined-variable
return rewrite(
wrapped_computation,
inputs=inputs,
infeed_queue=infeed_queue,
device_assignment=device_assignment,
name=name)
# pylint: enable=undefined-variable
def prune_unconnected_ops_from_xla(prune_graph: ops.Graph):
"""Prunes unconnected ops as listed in _UNCONNECTED_OPS_TO_PRUNE.
Args:
prune_graph: A tensorflow graph from which we wish to prune unconnected ops
as listed in _UNCONNECTED_OPS_TO_PRUNE. In general, these ops should have
no inputs and no consumers. These can often be left behind due to graph
construction rewiring (for instance TF-Hub). While they never execute,
they will cause XLA compile to fail so we strip them from XLA compile by
removing the tpu_replicate attribute.
"""
# Scan over the top level graph and all function graphs.
for graph in [prune_graph] + [
f for f in prune_graph._functions.values() # pylint: disable=protected-access
]:
if not isinstance(graph, ops.Graph):
continue
for op in graph.get_operations():
if op.type not in _UNCONNECTED_OPS_TO_PRUNE:
continue
outputs_consumed = False
for output in op.outputs:
if output.consumers():
outputs_consumed = True
break
if not outputs_consumed:
logging.info(
"Pruning OP %s of type %s from XLA Compile due to "
"it being disconnected.", op.name, op.type)
op._clear_attr(tpu_replication._TPU_REPLICATE_ATTR) # pylint: disable=protected-access